Sample selection algorithm to improve quality of genotyping from plasma-derived DNA: To separate the wheat from the chaff.

Plasma and serum samples were often the only biological material collected for earlier epidemiological studies. These studies have a huge informative content, especially due to their long follow-up and would be an invaluable treasure for genetic investigations. However, often no banked DNA is available. To use the small amounts of DNA present in plasma, in a first step, we applied magnetic bead technology to extract this DNA, followed by a whole-genome amplification (WGA) using phi29-polymerase. We assembled 88 sample pairs, each consisting of WGA plasma DNA and the corresponding whole-blood DNA. We genotyped nine highly polymorphic short tandem repeats (STRs) and 23 SNPs in both DNA sources. The average within-pair discordance was 3.8% for SNPs and 15.9% for STR genotypes, respectively. We developed an algorithm based on one-half of the sample pairs and validated on the other one-half to identify the samples with high WGA plasma DNA quality to assure low genotyping error and to exclude plasma DNA samples with insufficient quality: excluding samples showing homozygosity at five or more of the nine STR loci yielded exclusion of 22.7% of all samples and decreased average discordance for STR and SNP markers to 3.92% and 0.63%, respectively. For SNPs, this is very close to the error observed for genomic DNA in many laboratories. Our workflow and sample selection algorithm offers new opportunities to recover reliable DNA from stored plasma material. This algorithm is superior to testing the amount of input DNA